Intelligent digital oil and gas fields : concepts, collaboration, and right-time decisions

Intelligent Digital Oil and Gas Fields: Concepts, Collaboration, and Right-time Decisions delivers to the reader a roadmap through the fast-paced changes in the digital oil field landscape of technology in the form of new sensors, well mechanics such as downhole valves, data analytics and models for...

Full description

Saved in:
Bibliographic Details
Main Authors Carvajal, Gustavo (Author), Maucec, Marko (Author), Cullick, Stan (Author)
Format Electronic eBook
LanguageEnglish
Published Cambridge, MA : Gulf Professional Publishing, [2018]
EditionFirst edition.
Subjects
Oil fields > Equipment and supplies.
Gas fields > Equipment and supplies.
Oil fields > Data processing.
Gas fields > Data processing.
elektronické knihy
electronic books
Online AccessFull text
ISBN9780128047477
012804747X
0128046422
9780128046425
Physical Description1 online resource : color illustrations

Cover

Table of Contents:
  • Front Cover
  • Intelligent Digital Oil and Gas Fields: Concepts, Collaboration, and Right-time Decisions
  • Copyright
  • Dedication
  • Contents
  • Preface
  • Acknowledgments
  • Chapter One: Introduction to Digital Oil and Gas Field Systems
  • 1.1. What is a Digital Oil and Gas Field?
  • 1.2. DOF Key Technologies
  • 1.3. The Evolution of DOF
  • 1.4. DOF Operational Levels and Layers
  • 1.5. Main Components of the DOF
  • 1.5.1. Instrumentation, Remote Sensing, and Telemetry of Real-Time Processes
  • 1.5.2. Data Management and Data Transmission
  • 1.5.3. Workflow Automation
  • 1.5.4. User Interfaces and Visualization
  • 1.5.5. Collaboration and People Organization
  • 1.6. The Value of a DOF Implementation
  • 1.6.1. Industry Challenges
  • 1.6.2. How DOF Systems Address Challenges and Add Value
  • 1.6.3. DOF Benchmarks Across the World
  • 1.6.3.1. Smart Fields
  • 1.6.3.2. Field of the Future
  • 1.6.3.3. KwIDF Program
  • 1.6.3.4. Statoil's Integrated Operations
  • 1.6.3.5. I-Fields
  • 1.6.3.6. I-Field Practices
  • 1.6.3.7. COP's Integrated Operations
  • 1.7. Financial Potential of a DOF Implementation
  • 1.7.1. Field Description Example
  • 1.7.2. Cost Estimates
  • 1.7.3. Economic Parameters
  • 1.8. Tables Summarizing Major DOF Projects
  • References
  • Further Reading
  • Chapter Two: Instrumentation and Measurement
  • 2.1. Instrumentations for Measurement: Gauges and Flowmeters
  • 2.1.1. Surfaces Gauges
  • 2.1.2. Downhole Gauges
  • 2.1.3. Surface Flowmeters
  • 2.1.3.1. Types of Fluid properties Measured Over Time and Why
  • 2.1.3.2. Flowmeters: Principles of Measurement
  • 2.1.3.3. Criteria for Choosing a Flowmeter
  • 2.1.3.4. Key Factors to Consider in Flowmeter Selection
  • 2.1.3.5. Hybrid Single-Phase Flowmeters (Possible Combinations)
  • 2.1.3.6. Multiphase Flowmeter
  • Direct Flow Estimation
  • Virtual Flow Estimation.
  • 2.1.3.7. Flowmeter Selection
  • 2.2. Control Technology by Field Types
  • 2.2.1. General Control Technologies
  • 2.2.2. Mature Assets
  • 2.2.3. Deepwater Platforms and Floating Production Storage and Offloading
  • 2.2.4. Unconventional Assets
  • 2.3. Data Gathering and SCADA Architecture
  • 2.3.1. Well-Location Data Gathering and Telemetry
  • 2.3.2. Field Control Devices
  • 2.3.3. SCADA and Distributed Control System
  • 2.4. Special Note on Cybersecurity
  • 2.4.1. An Overview of Cyber-Attacks in O&G Companies
  • 2.4.2. Cybersecurity Challenges in DOF Systems
  • 2.4.3. The Actors, Their Motivation, and Kinds of Attacks
  • 2.4.4. Addressing Cybersecurity Challenges
  • 2.4.5. The Future on Cybersecurity
  • References
  • Further Reading
  • Chapter Three: Data Filtering and Conditioning*
  • 3.1. DOF System Data Validation and Management
  • 3.1.1. Data Processing
  • 3.2. Basic System for Cleansing, Filtering, Alerting, and Conditioning
  • 3.2.1. Data Validation System Architecture
  • 3.2.1.1. Rate of Change, Spike Detection, and Value Hold
  • 3.2.1.2. Out-Of-Range Detection and Value Clip
  • 3.2.1.3. Freeze Detection and Value Hold
  • 3.2.1.4. Statistical Detection and Value Hold
  • 3.2.1.5. Filtering
  • 3.2.2. Advanced Validation Techniques
  • 3.2.3. Model-Based Validation Methods
  • 3.2.4. Data Replacement Techniques
  • 3.2.5. Data Reconciliation
  • 3.2.5.1. Reconciliation Method: Example
  • 3.3. Conditioning
  • 3.3.1. The Level of Rate Acquisition (Data Frequency)
  • 3.3.2. Down Sampling Raw Data
  • 3.3.3. Summarizing From Raw Data
  • 3.3.4. Well and Equipment Status Detection Required for Sampling
  • 3.4. Conclusions
  • References
  • Chapter Four: Components of Artificial Intelligence and Data Analytics
  • 4.1. Introduction
  • 4.1.1. Artificial Intelligence: Overview of State of the Art in E&P.
  • 4.1.2. Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive, and Cognitive
  • 4.1.3. Big Data in E&P: Concepts and Platforms
  • 4.2. Intelligent Data Analytics and Visualization
  • 4.2.1. Data Mining
  • 4.2.2. Statistical and Machine Learning
  • 4.2.2.1. Artificial Neural Network
  • 4.2.2.2. Support Vector Machine
  • 4.2.2.3. Random Forest
  • 4.2.3. Visualization and Interactivity
  • 4.3. Applications to Digital Oil and Gas Fields
  • 4.3.1. Machine Learning and Predictive Analytics
  • 4.3.2. Data Mining, Multivariate, Root-Cause, and Performance Analysis
  • 4.3.3. Event Diagnostics and Failure Analysis
  • 4.3.4. Real-Time Analytics on Streaming Data
  • References
  • Further Reading
  • Chapter Five: Workflow Automation and Intelligent Control
  • 5.1. Introduction to Process Control
  • 5.2. Preparation of Automated Workflows for E&P
  • 5.2.1. Motivation for Automating E&P Workflows
  • 5.2.2. What Kinds of E&P Engineering Processes Should be Automated?
  • 5.2.3. Software Components of an E&P Workflow
  • 5.2.4. Modeling the Decision-Making Process
  • 5.2.5. Automated Workflow Levels of Complexity or Maturity
  • 5.2.6. The Ten Essential Steps to Build the Back End of an Automated Workflow
  • 5.2.7. Foundations of a Smart Workflow
  • 5.3. Virtual Multiphase Flow Metering-Based Model
  • 5.3.1. VFM Physical Models
  • 5.3.2. Building Blocks
  • 5.3.3. Self-Maintaining VFM for a Nonstationary Process
  • 5.3.4. Benefits and Disadvantages of Using VFM
  • 5.3.5. VFM Based on Artificial Intelligence Models
  • 5.4. Smart Production Surveillance for Daily Operations
  • 5.4.1. Business Model
  • 5.4.2. Main Components of Smart Production Surveillance
  • 5.4.3. UI Dashboard and Layout
  • 5.4.4. What Should Smart Production Surveillance Do?
  • 5.5. Well Test Validation and Production Performance in Right Time.
  • 5.5.1. Key Performance Indicators for Well Tests
  • 5.6. Diagnostics and Proactive Well Optimization With a Well Analysis Model
  • 5.6.1. Natural Flow
  • 5.6.2. ESP and PCP Systems
  • 5.6.3. Diagnostic Procedure
  • 5.6.4. Smart Diagnostics
  • 5.6.5. Artificial Lift Optimization
  • 5.7. Advisory and Tracking Actions
  • References
  • Further Reading
  • Chapter Six: Integrated Asset Management and Optimization Workflows
  • 6.1. Introduction to IAM and Optimization
  • 6.2. Optimization Approaches
  • 6.2.1. Single- vs. Multiobjective Optimization
  • 6.2.2. Local vs. Global Optimization
  • 6.2.2.1. Stochastic or (Meta) Heuristic Optimization
  • 6.2.3. Optimization Under Uncertainty
  • 6.3. Advanced Model Calibration With Assisted History Matching
  • 6.3.1. Model Parameterization and Dimensionality Reduction
  • 6.3.2. Bayesian Inference and Updating
  • 6.3.3. Data Assimilation
  • 6.3.4. Closed-Loop Model Updating
  • 6.4. Optimization of Modern DOF Assets
  • 6.4.1. Applications of IAM and Associated Work Processes
  • 6.4.2. Challenges and Ways Forward
  • References
  • Chapter Seven: Smart Wells and Techniques for Reservoir Monitoring
  • 7.1. Introduction to Smart Wells
  • 7.2. Types of Down-Hole Valves
  • 7.2.1. Passive Valves
  • 7.2.2. Autonomous Passive Valves
  • 7.2.3. Reactive-Actionable Valves
  • 7.3. Surface Data Acquisition and Control
  • 7.4. Smart Well Applications
  • 7.5. Smart Well Performance
  • 7.5.1. Production Test for Smart Wells
  • 7.5.2. Virtual PLT
  • 7.6. Smart Well Modeling and Control
  • 7.6.1. Single-Zone Control Analysis Using an ICV
  • 7.6.2. Multiple-Zone Control Analysis Using ICVs
  • 7.6.3. Coupling Wellbore and Gridded Simulators to Model ICVs
  • 7.6.4. Modeling ICDs for Oil Wells
  • 7.6.5. Modeling AICDs for Oil Wells
  • 7.7. Optimizing Field Production With Smart Wells
  • 7.7.1. Control Modes.
  • 7.8. Smart Improved Oil Recovery/Enhanced Oil Recovery Management
  • 7.8.1. WAG Injection Process
  • 7.8.1.1. WAG Process With ICV
  • 7.8.1.2. WAGCV Numerical Simulation
  • 7.8.2. Thermal Monitoring
  • 7.8.3. Automated EOR/Chemical Process
  • References
  • Further Reading
  • Chapter Eight: Transitioning to Effective DOF Enabled by Collaboration and Management of Change
  • 8.1. Transition to DOF
  • 8.1.1. Planning a DOF Implementation
  • 8.1.2. Key Performance Metrics for DOF Implementation
  • 8.2. Collaborative Work Environment
  • 8.2.1. Physical Space
  • 8.2.2. Value of Collaborative Work Processes
  • 8.2.3. Mobility
  • 8.2.4. Examples: Collaboration and Mobility in Practice
  • 8.3. Management of Change
  • 8.3.1. Collaboration in Practice: "A Day in the Life" of a DOF Operation
  • 8.3.2. Change Management: High-Performance Teams
  • 8.3.2.1. Competency Development
  • 8.3.2.2. Competency Management
  • 8.3.2.3. Knowledge Management
  • 8.3.2.4. Team Synergy, Behaviors, and Role Transition
  • 8.4. Conclusion
  • References
  • Further Reading
  • Chapter Nine: The Future Digital Oil Field
  • 9.1. Ubiquitous Sensors (IIoT)
  • 9.1.1. Nanosensors
  • 9.2. Data Everywhere
  • 9.3. Next-Generation Analytics
  • 9.4. Automation and Remote Control
  • 9.4.1. Wireless Technology
  • 9.4.2. Drones
  • 9.5. Knowledge Everywhere: Knowledge Capture and People Resources
  • 9.5.1. Capturing Knowledge in New Ways
  • 9.5.2. Delivering DOF to the Business
  • 9.6. Integrated Reservoir Decisions
  • 9.6.1. Big Data and Big Models
  • 9.6.2. Optimizing Optimization and the "Closed Loop
  • 9.6.3. High-Performance Computing for the Future DOF
  • 9.7. Collaboration, Mobility, and Machine-Human Interface
  • 9.7.1. Mobility and Collaboration
  • 9.7.2. Virtual and Augmented Reality Enable Immersive Collaboration
  • 9.7.3. Human-Machine Interface
  • 9.8. Summing Up and Looking Ahead.

Similar Items